Project Summary Neurodegenerative disease processes leading to Alzheimer?s disease (AD) start decades before they are clinically observed and are difficult to treat at the stage of a formal diagnosis. It is therefore imperative to determine risk factors early in the course of the disease. Accumulating evidence suggests that concussion, or mild TBI, is associated with dementia when combined with additional risk factors such as genetic risk for AD, repetitive injury, and age at injury. As mild TBI has widespread prevalence and represents the majority of all head injuries, the potential link to neurodegenerative disease presents a major public health problem. However, there is a fundamental gap in understanding the mechanisms by which mild TBI confers risk for AD and other neurodegenerative diseases. The long-term goal of this project is to identify preclinical biomarkers for neurodegenerative disease following mild TBI that provide insight into the mechanisms linking mild TBI to neurodegenerative disease. The overall objective of the current application is to identify genetic, epigenetic, and blood-based protein biomarkers of neurodegeneration that are associated with MRI brain metrics of AD pathology longitudinally following mild TBI. The central hypothesis of the proposed project is that mild TBI confers risk for AD and other neurodegenerative diseases when combined with genetic, epigenetic, and other risk factors. We will test our hypothesis by pursuing three specific aims: Aim 1. Determine the longitudinal effects of genetic risk for AD and mild TBI on MRI-based measures of early AD pathology. Aim 2. Identify epigenetic markers of AD and mild TBI that are associated with MRI-based measures of early AD pathology. Aim 3. Identify fluid biomarkers of neurodegenerative disease in mild TBI that are associated with MRI-based measures of early AD pathology. The proposed research is significant because it is expected to advance understanding of who may be at increased risk for AD in the preclinical course of the disease and, in the long term, facilitate the development of a temporal model of disease progression that outlines when each biomarker becomes predictive of AD. The approach is innovative because it leverages longitudinal and multimodal data (genetics, epigenetics, blood-based proteins, MRI, neurocognitive data) from an unparalleled database of close to 600 recent war veterans. Ultimately, such knowledge has the potential to inform clinical judgments regarding who may need to start a treatment regimen to prevent the onset of dementia.